Title :
Template matching approach to content based image indexing by low dimensional Euclidean embedding
Author :
Schweitzer, Haim
Author_Institution :
Texas Univ., Dallas, TX, USA
Abstract :
Content based indexing is computed from input that consists of matching values between images and templates. The key idea is to embed both images and templates in a low-dimensional Euclidean space so that matching between embedded images and embedded templates approximates the given input. It is shown that such embedding can be computed by means of a singular value decomposition of the input matrix. Classic principal component analysis is shown to be a special case of the proposed technique, corresponding to the case where the templates and the images are the same
Keywords :
content-based retrieval; database indexing; image matching; principal component analysis; singular value decomposition; content based image indexing; content based indexing; embedded images; embedded templates; images; low dimensional Euclidean embedding; low-dimensional Euclidean space; principal component analysis; singular value decomposition; template matching approach; templates; Application software; Computer applications; Covariance matrix; Embedded computing; Indexing; Principal component analysis;
Conference_Titel :
Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-1143-0
DOI :
10.1109/ICCV.2001.937676
